Development and validation of a new guidance device for lateral approach stereotactic breast biopsy
Bibliographic record
Abstract
Stereotactic breast biopsy (SBB) is the gold standard for minimally invasive breast cancer diagnosis. Current systems rely on one of two methods for needle insertion: A vertical approach (perpendicular to the breast compression plate) or a lateral approach (parallel to the compression plate), While the vertical approach is more frequently used, it is not feasible in patients with thin breasts (<3 cm thick after compression) or with superficial lesions. Further, existing SBB guidance hardware provides at most one degree of rotational freedom in the needle trajectory, and as such requires a separate skin incision for each biopsy target. The authors present a new design of lateral guidance device for SBB, which addresses the limitations of the vertical approach and provides improvements over the existing lateral guidance hardware. Specifically, the new device provides (1) an adjustable rigid needle support to minimize needle deflection within the breast and (2) an additional degree of rotational freedom in the needle trajectory, allowing the radiologist to sample multiple targets through a single skin incision. This device was compared to a commercial lateral guidance device in a series of phantom experiments. Needle placement error using each device was measured in agar phantoms for needle insertions at lateral depths of 2 and 5 cm. The biopsy success rate for each device was then estimated by performing biopsy procedures in commercial SBB phantoms. SBB performed with the new lateral guidance device provided reduced needle placement error relative to the commercial lateral guidance device (0.89 +/- 0.22 vs 1.75 +/- 0.35 mm for targets at 2 cm depth; 1.94 +/- 0.20 vs 3.21 +/- 0.31 mm for targets at 5 cm depth). The new lateral guidance device also provided improved biopsy accuracy in SBB procedures compared to the commercial lateral guidance device (100% vs 58% success rate). Finally, experiments were performed to demonstrate that the new device can accurately sample lesions within thin breast phantoms and multiple lesions through a single incision point. This device can be incorporated directly into the clinical SBB procedural workflow, with no additional electrical hardware, software, postprocessing, or image analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".